Overview

Dataset statistics

Number of variables15
Number of observations20091
Missing cells4050
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory120.0 B

Variable types

Numeric6
Text4
Categorical4
DateTime1

Alerts

income has 612 (3.0%) missing valuesMissing
payment_mode has 697 (3.5%) missing valuesMissing
date has 491 (2.4%) missing valuesMissing
category has 1084 (5.4%) missing valuesMissing
stock has 711 (3.5%) missing valuesMissing

Reproduction

Analysis started2026-02-24 09:05:13.923770
Analysis finished2026-02-24 09:05:20.113977
Duration6.19 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5000
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2513.8754
Minimum1
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.1 KiB
2026-02-24T14:35:20.183448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile258
Q11260
median2524
Q33771
95-th percentile4750
Maximum5000
Range4999
Interquartile range (IQR)2511

Descriptive statistics

Standard deviation1444.2631
Coefficient of variation (CV)0.57451658
Kurtosis-1.20165
Mean2513.8754
Median Absolute Deviation (MAD)1256
Skewness-0.013086578
Sum50506271
Variance2085895.9
MonotonicityIncreasing
2026-02-24T14:35:20.301258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136614
 
0.1%
13813
 
0.1%
60513
 
0.1%
291613
 
0.1%
494812
 
0.1%
109311
 
0.1%
457811
 
0.1%
425611
 
0.1%
268411
 
0.1%
487111
 
0.1%
Other values (4990)19971
99.4%
ValueCountFrequency (%)
13
 
< 0.1%
21
 
< 0.1%
39
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
68
< 0.1%
75
< 0.1%
85
< 0.1%
95
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
50006
< 0.1%
49994
< 0.1%
49985
< 0.1%
49971
 
< 0.1%
49961
 
< 0.1%
49954
< 0.1%
49944
< 0.1%
49933
< 0.1%
49921
 
< 0.1%
49915
< 0.1%

name
Text

Distinct200
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size157.1 KiB
2026-02-24T14:35:20.510942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.968941
Min length8

Characters and Unicode

Total characters220377
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArjun Verma
2nd rowArjun Verma
3rd rowArjun Verma
4th rowShaurya Khan
5th rowAnika Verma
ValueCountFrequency (%)
reddy2193
 
5.5%
sharma2145
 
5.3%
iyer2052
 
5.1%
mehta2018
 
5.0%
gupta1992
 
5.0%
singh1972
 
4.9%
patel1968
 
4.9%
verma1964
 
4.9%
khan1938
 
4.8%
nair1849
 
4.6%
Other values (20)20091
50.0%
2026-02-24T14:35:20.826153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a43986
20.0%
20091
 
9.1%
r15844
 
7.2%
h14101
 
6.4%
y13355
 
6.1%
i13096
 
5.9%
n12930
 
5.9%
e11277
 
5.1%
t7056
 
3.2%
S6958
 
3.2%
Other values (20)61683
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)220377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a43986
20.0%
20091
 
9.1%
r15844
 
7.2%
h14101
 
6.4%
y13355
 
6.1%
i13096
 
5.9%
n12930
 
5.9%
e11277
 
5.1%
t7056
 
3.2%
S6958
 
3.2%
Other values (20)61683
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)220377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a43986
20.0%
20091
 
9.1%
r15844
 
7.2%
h14101
 
6.4%
y13355
 
6.1%
i13096
 
5.9%
n12930
 
5.9%
e11277
 
5.1%
t7056
 
3.2%
S6958
 
3.2%
Other values (20)61683
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)220377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a43986
20.0%
20091
 
9.1%
r15844
 
7.2%
h14101
 
6.4%
y13355
 
6.1%
i13096
 
5.9%
n12930
 
5.9%
e11277
 
5.1%
t7056
 
3.2%
S6958
 
3.2%
Other values (20)61683
28.0%

age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.627395
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.1 KiB
2026-02-24T14:35:20.925647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median44
Q357
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.029731
Coefficient of variation (CV)0.34450214
Kurtosis-1.2120725
Mean43.627395
Median Absolute Deviation (MAD)13
Skewness-0.01223145
Sum876518
Variance225.89282
MonotonicityNot monotonic
2026-02-24T14:35:21.042945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43487
 
2.4%
46468
 
2.3%
66462
 
2.3%
20437
 
2.2%
28429
 
2.1%
30426
 
2.1%
38425
 
2.1%
40423
 
2.1%
19422
 
2.1%
62421
 
2.1%
Other values (42)15691
78.1%
ValueCountFrequency (%)
18327
1.6%
19422
2.1%
20437
2.2%
21416
2.1%
22338
1.7%
23373
1.9%
24369
1.8%
25365
1.8%
26328
1.6%
27401
2.0%
ValueCountFrequency (%)
69366
1.8%
68411
2.0%
67296
1.5%
66462
2.3%
65412
2.1%
64387
1.9%
63386
1.9%
62421
2.1%
61408
2.0%
60403
2.0%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.1 KiB
Male
6805 
Other
6795 
Female
6491 

Length

Max length6
Median length5
Mean length4.9843711
Min length4

Characters and Unicode

Total characters100141
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male6805
33.9%
Other6795
33.8%
Female6491
32.3%

Length

2026-02-24T14:35:21.155471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-24T14:35:21.223263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male6805
33.9%
other6795
33.8%
female6491
32.3%

Most occurring characters

ValueCountFrequency (%)
e26582
26.5%
a13296
13.3%
l13296
13.3%
M6805
 
6.8%
O6795
 
6.8%
t6795
 
6.8%
h6795
 
6.8%
r6795
 
6.8%
F6491
 
6.5%
m6491
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)100141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e26582
26.5%
a13296
13.3%
l13296
13.3%
M6805
 
6.8%
O6795
 
6.8%
t6795
 
6.8%
h6795
 
6.8%
r6795
 
6.8%
F6491
 
6.5%
m6491
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e26582
26.5%
a13296
13.3%
l13296
13.3%
M6805
 
6.8%
O6795
 
6.8%
t6795
 
6.8%
h6795
 
6.8%
r6795
 
6.8%
F6491
 
6.5%
m6491
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e26582
26.5%
a13296
13.3%
l13296
13.3%
M6805
 
6.8%
O6795
 
6.8%
t6795
 
6.8%
h6795
 
6.8%
r6795
 
6.8%
F6491
 
6.5%
m6491
 
6.5%

city
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.1 KiB
Mumbai
2109 
Bangalore
2105 
Pune
2071 
Hyderabad
2065 
Delhi
2049 
Other values (5)
9692 

Length

Max length9
Median length7
Mean length6.6996665
Min length4

Characters and Unicode

Total characters134603
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJaipur
2nd rowJaipur
3rd rowJaipur
4th rowHyderabad
5th rowSurat

Common Values

ValueCountFrequency (%)
Mumbai2109
10.5%
Bangalore2105
10.5%
Pune2071
10.3%
Hyderabad2065
10.3%
Delhi2049
10.2%
Kolkata1993
9.9%
Jaipur1962
9.8%
Ahmedabad1927
9.6%
Surat1923
9.6%
Chennai1887
9.4%

Length

2026-02-24T14:35:21.304947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-24T14:35:21.388517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mumbai2109
10.5%
bangalore2105
10.5%
pune2071
10.3%
hyderabad2065
10.3%
delhi2049
10.2%
kolkata1993
9.9%
jaipur1962
9.8%
ahmedabad1927
9.6%
surat1923
9.6%
chennai1887
9.4%

Most occurring characters

ValueCountFrequency (%)
a24061
17.9%
e12104
 
9.0%
u8065
 
6.0%
r8055
 
6.0%
i8007
 
5.9%
d7984
 
5.9%
n7950
 
5.9%
l6147
 
4.6%
b6101
 
4.5%
h5863
 
4.4%
Other values (17)40266
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)134603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a24061
17.9%
e12104
 
9.0%
u8065
 
6.0%
r8055
 
6.0%
i8007
 
5.9%
d7984
 
5.9%
n7950
 
5.9%
l6147
 
4.6%
b6101
 
4.5%
h5863
 
4.4%
Other values (17)40266
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)134603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a24061
17.9%
e12104
 
9.0%
u8065
 
6.0%
r8055
 
6.0%
i8007
 
5.9%
d7984
 
5.9%
n7950
 
5.9%
l6147
 
4.6%
b6101
 
4.5%
h5863
 
4.4%
Other values (17)40266
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)134603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a24061
17.9%
e12104
 
9.0%
u8065
 
6.0%
r8055
 
6.0%
i8007
 
5.9%
d7984
 
5.9%
n7950
 
5.9%
l6147
 
4.6%
b6101
 
4.5%
h5863
 
4.4%
Other values (17)40266
29.9%

income
Real number (ℝ)

Missing 

Distinct4835
Distinct (%)24.8%
Missing612
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean652747.93
Minimum100005
Maximum9978234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.1 KiB
2026-02-24T14:35:21.527258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100005
5-th percentile125656
Q1241539.5
median449146
Q3791307
95-th percentile1638781
Maximum9978234
Range9878229
Interquartile range (IQR)549767.5

Descriptive statistics

Standard deviation833133.56
Coefficient of variation (CV)1.2763481
Kurtosis52.977278
Mean652747.93
Median Absolute Deviation (MAD)241129
Skewness6.2197965
Sum1.2714877 × 1010
Variance6.9411152 × 1011
MonotonicityNot monotonic
2026-02-24T14:35:21.657682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71496714
 
0.1%
60575713
 
0.1%
18401513
 
0.1%
102856313
 
0.1%
35443113
 
0.1%
51602212
 
0.1%
26011012
 
0.1%
16585211
 
0.1%
55777611
 
0.1%
12591311
 
0.1%
Other values (4825)19356
96.3%
(Missing)612
 
3.0%
ValueCountFrequency (%)
10000510
< 0.1%
1000931
 
< 0.1%
1001203
 
< 0.1%
1002212
 
< 0.1%
1003586
< 0.1%
1003944
 
< 0.1%
1006189
< 0.1%
1006774
 
< 0.1%
1007836
< 0.1%
1008226
< 0.1%
ValueCountFrequency (%)
99782343
< 0.1%
99082403
< 0.1%
98090453
< 0.1%
97798972
 
< 0.1%
97773765
< 0.1%
97560046
< 0.1%
97422563
< 0.1%
96492922
 
< 0.1%
93202952
 
< 0.1%
91495703
< 0.1%
Distinct20000
Distinct (%)100.0%
Missing91
Missing (%)0.5%
Memory size157.1 KiB
2026-02-24T14:35:22.068810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20000 ?
Unique (%)100.0%

Sample

1st rowT002318
2nd rowT004426
3rd rowT012020
4th rowT004924
5th rowT002934
ValueCountFrequency (%)
t0023181
 
< 0.1%
t0044261
 
< 0.1%
t0096741
 
< 0.1%
t0106811
 
< 0.1%
t0115731
 
< 0.1%
t0118221
 
< 0.1%
t0066571
 
< 0.1%
t0146411
 
< 0.1%
t0196661
 
< 0.1%
t0044531
 
< 0.1%
Other values (19990)19990
> 99.9%
2026-02-24T14:35:22.550872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
037999
27.1%
T20000
14.3%
118000
12.9%
28001
 
5.7%
38000
 
5.7%
88000
 
5.7%
48000
 
5.7%
68000
 
5.7%
98000
 
5.7%
78000
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
037999
27.1%
T20000
14.3%
118000
12.9%
28001
 
5.7%
38000
 
5.7%
88000
 
5.7%
48000
 
5.7%
68000
 
5.7%
98000
 
5.7%
78000
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
037999
27.1%
T20000
14.3%
118000
12.9%
28001
 
5.7%
38000
 
5.7%
88000
 
5.7%
48000
 
5.7%
68000
 
5.7%
98000
 
5.7%
78000
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
037999
27.1%
T20000
14.3%
118000
12.9%
28001
 
5.7%
38000
 
5.7%
88000
 
5.7%
48000
 
5.7%
68000
 
5.7%
98000
 
5.7%
78000
 
5.7%
Distinct1000
Distinct (%)5.0%
Missing91
Missing (%)0.5%
Memory size157.1 KiB
2026-02-24T14:35:22.974975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters100000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP0468
2nd rowP0810
3rd rowP0821
4th rowP0633
5th rowP0350
ValueCountFrequency (%)
p090536
 
0.2%
p042835
 
0.2%
p064433
 
0.2%
p028132
 
0.2%
p013632
 
0.2%
p036231
 
0.2%
p074931
 
0.2%
p011931
 
0.2%
p071731
 
0.2%
p068331
 
0.2%
Other values (990)19677
98.4%
2026-02-24T14:35:23.466879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
025847
25.8%
P20000
20.0%
76142
 
6.1%
96067
 
6.1%
46015
 
6.0%
16004
 
6.0%
86002
 
6.0%
35997
 
6.0%
65986
 
6.0%
25980
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
025847
25.8%
P20000
20.0%
76142
 
6.1%
96067
 
6.1%
46015
 
6.0%
16004
 
6.0%
86002
 
6.0%
35997
 
6.0%
65986
 
6.0%
25980
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
025847
25.8%
P20000
20.0%
76142
 
6.1%
96067
 
6.1%
46015
 
6.0%
16004
 
6.0%
86002
 
6.0%
35997
 
6.0%
65986
 
6.0%
25980
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
025847
25.8%
P20000
20.0%
76142
 
6.1%
96067
 
6.1%
46015
 
6.0%
16004
 
6.0%
86002
 
6.0%
35997
 
6.0%
65986
 
6.0%
25980
 
6.0%

amount
Real number (ℝ)

Distinct4826
Distinct (%)24.1%
Missing91
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1728.4613
Minimum100
Maximum49912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.1 KiB
2026-02-24T14:35:23.572290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile177
Q1532.75
median1142
Q32190
95-th percentile4700
Maximum49912
Range49812
Interquartile range (IQR)1657.25

Descriptive statistics

Standard deviation2624.8465
Coefficient of variation (CV)1.518603
Kurtosis138.23852
Mean1728.4613
Median Absolute Deviation (MAD)724
Skewness9.8327234
Sum34569226
Variance6889818.9
MonotonicityNot monotonic
2026-02-24T14:35:23.689521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16123
 
0.1%
40922
 
0.1%
24322
 
0.1%
12721
 
0.1%
16021
 
0.1%
50621
 
0.1%
11420
 
0.1%
18020
 
0.1%
19620
 
0.1%
13320
 
0.1%
Other values (4816)19790
98.5%
(Missing)91
 
0.5%
ValueCountFrequency (%)
10018
0.1%
10113
0.1%
10210
< 0.1%
1036
 
< 0.1%
1043
 
< 0.1%
10516
0.1%
10615
0.1%
1078
< 0.1%
10816
0.1%
1099
< 0.1%
ValueCountFrequency (%)
499121
< 0.1%
489781
< 0.1%
487051
< 0.1%
481451
< 0.1%
478611
< 0.1%
476941
< 0.1%
472811
< 0.1%
470711
< 0.1%
464111
< 0.1%
463641
< 0.1%

payment_mode
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing697
Missing (%)3.5%
Memory size157.1 KiB
Net Banking
3948 
Credit Card
3918 
Cash
3887 
UPI
3856 
Debit Card
3785 

Length

Max length11
Median length10
Mean length7.8112818
Min length3

Characters and Unicode

Total characters151492
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUPI
2nd rowNet Banking
3rd rowCash
4th rowCash
5th rowUPI

Common Values

ValueCountFrequency (%)
Net Banking3948
19.7%
Credit Card3918
19.5%
Cash3887
19.3%
UPI3856
19.2%
Debit Card3785
18.8%
(Missing)697
 
3.5%

Length

2026-02-24T14:35:23.811649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-24T14:35:23.895690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
card7703
24.8%
banking3948
12.7%
net3948
12.7%
credit3918
12.6%
cash3887
12.5%
upi3856
12.4%
debit3785
12.2%

Most occurring characters

ValueCountFrequency (%)
a15538
10.3%
C15508
10.2%
e11651
 
7.7%
t11651
 
7.7%
i11651
 
7.7%
11651
 
7.7%
d11621
 
7.7%
r11621
 
7.7%
n7896
 
5.2%
N3948
 
2.6%
Other values (10)38756
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)151492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a15538
10.3%
C15508
10.2%
e11651
 
7.7%
t11651
 
7.7%
i11651
 
7.7%
11651
 
7.7%
d11621
 
7.7%
r11621
 
7.7%
n7896
 
5.2%
N3948
 
2.6%
Other values (10)38756
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)151492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a15538
10.3%
C15508
10.2%
e11651
 
7.7%
t11651
 
7.7%
i11651
 
7.7%
11651
 
7.7%
d11621
 
7.7%
r11621
 
7.7%
n7896
 
5.2%
N3948
 
2.6%
Other values (10)38756
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)151492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a15538
10.3%
C15508
10.2%
e11651
 
7.7%
t11651
 
7.7%
i11651
 
7.7%
11651
 
7.7%
d11621
 
7.7%
r11621
 
7.7%
n7896
 
5.2%
N3948
 
2.6%
Other values (10)38756
25.6%

date
Date

Missing 

Distinct365
Distinct (%)1.9%
Missing491
Missing (%)2.4%
Memory size157.1 KiB
Minimum2025-01-01 00:00:00
Maximum2025-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-24T14:35:23.999737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:24.103950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1000
Distinct (%)5.0%
Missing91
Missing (%)0.5%
Memory size157.1 KiB
2026-02-24T14:35:24.388395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.89465
Min length9

Characters and Unicode

Total characters217893
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProduct_468
2nd rowProduct_810
3rd rowProduct_821
4th rowProduct_633
5th rowProduct_350
ValueCountFrequency (%)
product_90536
 
0.2%
product_42835
 
0.2%
product_64433
 
0.2%
product_28132
 
0.2%
product_13632
 
0.2%
product_36231
 
0.2%
product_74931
 
0.2%
product_11931
 
0.2%
product_71731
 
0.2%
product_68331
 
0.2%
Other values (990)19677
98.4%
2026-02-24T14:35:24.771489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P20000
9.2%
r20000
9.2%
o20000
9.2%
d20000
9.2%
u20000
9.2%
c20000
9.2%
t20000
9.2%
_20000
9.2%
76142
 
2.8%
96067
 
2.8%
Other values (8)45684
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)217893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P20000
9.2%
r20000
9.2%
o20000
9.2%
d20000
9.2%
u20000
9.2%
c20000
9.2%
t20000
9.2%
_20000
9.2%
76142
 
2.8%
96067
 
2.8%
Other values (8)45684
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)217893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P20000
9.2%
r20000
9.2%
o20000
9.2%
d20000
9.2%
u20000
9.2%
c20000
9.2%
t20000
9.2%
_20000
9.2%
76142
 
2.8%
96067
 
2.8%
Other values (8)45684
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)217893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P20000
9.2%
r20000
9.2%
o20000
9.2%
d20000
9.2%
u20000
9.2%
c20000
9.2%
t20000
9.2%
_20000
9.2%
76142
 
2.8%
96067
 
2.8%
Other values (8)45684
21.0%

category
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing1084
Missing (%)5.4%
Memory size157.1 KiB
Computer
4066 
Mobile Accessories
4065 
Wearable
3973 
Audio
3962 
Electronics
2941 

Length

Max length18
Median length11
Mean length9.9775346
Min length5

Characters and Unicode

Total characters189643
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComputer
2nd rowElectronics
3rd rowAudio
4th rowComputer
5th rowElectronics

Common Values

ValueCountFrequency (%)
Computer4066
20.2%
Mobile Accessories4065
20.2%
Wearable3973
19.8%
Audio3962
19.7%
Electronics2941
14.6%
(Missing)1084
 
5.4%

Length

2026-02-24T14:35:24.860758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-24T14:35:24.930492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
computer4066
17.6%
mobile4065
17.6%
accessories4065
17.6%
wearable3973
17.2%
audio3962
17.2%
electronics2941
12.7%

Most occurring characters

ValueCountFrequency (%)
e27148
14.3%
o19099
 
10.1%
s15136
 
8.0%
r15045
 
7.9%
i15033
 
7.9%
c14012
 
7.4%
l10979
 
5.8%
b8038
 
4.2%
u8028
 
4.2%
A8027
 
4.2%
Other values (11)49098
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)189643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e27148
14.3%
o19099
 
10.1%
s15136
 
8.0%
r15045
 
7.9%
i15033
 
7.9%
c14012
 
7.4%
l10979
 
5.8%
b8038
 
4.2%
u8028
 
4.2%
A8027
 
4.2%
Other values (11)49098
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)189643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e27148
14.3%
o19099
 
10.1%
s15136
 
8.0%
r15045
 
7.9%
i15033
 
7.9%
c14012
 
7.4%
l10979
 
5.8%
b8038
 
4.2%
u8028
 
4.2%
A8027
 
4.2%
Other values (11)49098
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)189643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e27148
14.3%
o19099
 
10.1%
s15136
 
8.0%
r15045
 
7.9%
i15033
 
7.9%
c14012
 
7.4%
l10979
 
5.8%
b8038
 
4.2%
u8028
 
4.2%
A8027
 
4.2%
Other values (11)49098
25.9%

price
Real number (ℝ)

Distinct778
Distinct (%)3.9%
Missing91
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2921.1703
Minimum105
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.1 KiB
2026-02-24T14:35:25.034656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile235
Q1889
median1702
Q32507
95-th percentile10000
Maximum50000
Range49895
Interquartile range (IQR)1618

Descriptive statistics

Standard deviation5693.9289
Coefficient of variation (CV)1.9491945
Kurtosis43.975718
Mean2921.1703
Median Absolute Deviation (MAD)813
Skewness6.1033453
Sum58423405
Variance32420827
MonotonicityNot monotonic
2026-02-24T14:35:25.145711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100001565
 
7.8%
25000209
 
1.0%
50000190
 
0.9%
166584
 
0.4%
248776
 
0.4%
100675
 
0.4%
42775
 
0.4%
229972
 
0.4%
126872
 
0.4%
302272
 
0.4%
Other values (768)17510
87.2%
(Missing)91
 
0.5%
ValueCountFrequency (%)
10522
0.1%
10633
0.2%
10814
 
0.1%
11324
0.1%
11653
0.3%
11722
0.1%
12122
0.1%
12516
 
0.1%
12823
0.1%
12919
 
0.1%
ValueCountFrequency (%)
50000190
 
0.9%
25000209
 
1.0%
100001565
7.8%
309940
 
0.2%
309818
 
0.1%
309729
 
0.1%
309024
 
0.1%
308621
 
0.1%
308019
 
0.1%
307422
 
0.1%

stock
Real number (ℝ)

Missing 

Distinct101
Distinct (%)0.5%
Missing711
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean49.855315
Minimum-5
Maximum100
Zeros0
Zeros (%)0.0%
Negative196
Negative (%)1.0%
Memory size157.1 KiB
2026-02-24T14:35:25.263513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile4
Q124
median49
Q375
95-th percentile95
Maximum100
Range105
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.443116
Coefficient of variation (CV)0.59057126
Kurtosis-1.1957909
Mean49.855315
Median Absolute Deviation (MAD)26
Skewness-0.012968924
Sum966196
Variance866.89707
MonotonicityNot monotonic
2026-02-24T14:35:25.374885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42386
 
1.9%
74384
 
1.9%
14355
 
1.8%
89335
 
1.7%
70324
 
1.6%
93318
 
1.6%
37308
 
1.5%
49294
 
1.5%
21285
 
1.4%
98282
 
1.4%
Other values (91)16109
80.2%
(Missing)711
 
3.5%
ValueCountFrequency (%)
-5196
1.0%
1208
1.0%
2144
0.7%
3247
1.2%
4229
1.1%
586
 
0.4%
6270
1.3%
7177
0.9%
8213
1.1%
9221
1.1%
ValueCountFrequency (%)
100132
0.7%
99196
1.0%
98282
1.4%
97123
 
0.6%
96147
0.7%
95214
1.1%
94154
0.8%
93318
1.6%
92258
1.3%
91103
 
0.5%

Interactions

2026-02-24T14:35:19.001239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:15.663108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.198285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.730729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.266033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.444705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:19.085244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:15.754357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.295585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.819974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.352295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.540822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:19.169307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:15.843338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.385664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.915839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.442787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.639380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:19.253397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:15.929604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.473731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.000809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.534353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.738744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:19.338305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.017884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.560531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.091489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.618325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.832019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:19.424852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.105363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:16.646069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:17.179224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.346491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-24T14:35:18.916074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-24T14:35:25.458187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageamountcategorycitycustomer_idgenderincomepayment_modepricestock
age1.0000.0010.0020.041-0.0070.040-0.0220.0010.001-0.010
amount0.0011.0000.0000.0030.0000.000-0.0040.0000.008-0.011
category0.0020.0001.0000.0000.0140.0030.0000.0000.3830.090
city0.0410.0030.0001.0000.0440.0500.0350.0100.0070.000
customer_id-0.0070.0000.0140.0441.0000.0450.0030.012-0.0020.004
gender0.0400.0000.0030.0500.0451.0000.0360.0000.0000.000
income-0.022-0.0040.0000.0350.0030.0361.0000.000-0.0030.013
payment_mode0.0010.0000.0000.0100.0120.0000.0001.0000.0000.000
price0.0010.0080.3830.007-0.0020.000-0.0030.0001.0000.008
stock-0.010-0.0110.0900.0000.0040.0000.0130.0000.0081.000

Missing values

2026-02-24T14:35:19.571455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-24T14:35:19.727793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-24T14:35:19.943295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idnameagegendercityincometransaction_idproduct_idamountpayment_modedateproduct_namecategorypricestock
01Arjun Verma56FemaleJaipur896150.0T002318P0468281.0UPI2025-11-15Product_468Computer1006.064.0
11Arjun Verma56FemaleJaipur896150.0T004426P0810822.0Net Banking2025-09-08Product_810Electronics10000.0NaN
21Arjun Verma56FemaleJaipur896150.0T012020P0821680.0Cash2025-12-18Product_821Audio2710.072.0
32Shaurya Khan32MaleHyderabad758372.0T004924P06334006.0Cash2025-10-14Product_633Computer2734.021.0
43Anika Verma38FemaleSurat184812.0T002934P03502645.0UPI2025-03-06Product_350Electronics25000.0-5.0
53Anika Verma38FemaleSurat184812.0T004198P09391774.0Debit Card2025-03-23Product_939Mobile Accessories613.03.0
63Anika Verma38FemaleSurat184812.0T008754P02053322.0UPI2025-11-10Product_205Electronics294.05.0
73Anika Verma38FemaleSurat184812.0T009173P08851722.0UPI2025-07-02Product_885Electronics1047.010.0
83Anika Verma38FemaleSurat184812.0T010772P0884392.0Cash2025-05-02Product_884Mobile Accessories2253.050.0
93Anika Verma38FemaleSurat184812.0T010879P08545190.0UPI2025-12-08Product_854Mobile Accessories532.052.0
customer_idnameagegendercityincometransaction_idproduct_idamountpayment_modedateproduct_namecategorypricestock
200814999Aarav Sharma19FemaleDelhi678984.0T013342P0001616.0Debit Card2025-09-18Product_1Audio1940.053.0
200824999Aarav Sharma19FemaleDelhi678984.0T015424P0943319.0Cash2025-09-25Product_943Computer1387.037.0
200834999Aarav Sharma19FemaleDelhi678984.0T015643P00264377.0Credit Card2025-06-19Product_26Audio2702.014.0
200844999Aarav Sharma19FemaleDelhi678984.0T016522P02236801.0Credit Card2025-05-10Product_223Computer2678.07.0
200855000Diya Nair40MaleJaipur7028217.0T000041P0186295.0Net Banking2025-08-23Product_186Audio213.03.0
200865000Diya Nair40MaleJaipur7028217.0T002479P02241274.0UPI2025-04-06Product_224Mobile Accessories2296.048.0
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